{
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  "metadata": {
    "colab": {
      "name": "1026.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yananma/5_programs_per_day/blob/master/1026.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vS9Ulqu2JHtB",
        "colab_type": "text"
      },
      "source": [
        "## d2l Chapter 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93Ia91Nfd20C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install mxnet\n",
        "import mxnet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JHt82jDkRfES",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mxnet import nd \n",
        "from time import time \n",
        "\n",
        "a = nd.ones(shape=1000)\n",
        "b = nd.ones(shape=1000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m9WZbScuSgOC",
        "colab_type": "code",
        "outputId": "a1c41265-470b-49e4-fa53-3de4d87a0988",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "start = time()\n",
        "c = nd.zeros(shape=1000)\n",
        "for i in range(1000):\n",
        "    c[i] = a[i] + b[i]\n",
        "time() - start"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.19153738021850586"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-uNmtB1ES2oc",
        "colab_type": "code",
        "outputId": "2e73c095-14fb-4009-9316-56de081db626",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "start = time()\n",
        "d = a + b \n",
        "time() - start"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0010044574737548828"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JhkeE-x7S-3b",
        "colab_type": "code",
        "outputId": "5b9e7a4e-1346-403e-c2b6-fd02130b1c9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "a = nd.ones(shape=3)\n",
        "b = 10 \n",
        "a + b"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[11. 11. 11.]\n",
              "<NDArray 3 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nP13snSqXzg8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from IPython import display \n",
        "import matplotlib.pyplot as plt \n",
        "from mxnet import autograd, nd \n",
        "import random\n",
        "%matplotlib inline "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cxHskX0RZNrF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_inputs = 2 \n",
        "num_examples = 1000 \n",
        "true_w = [2, -3.4]\n",
        "true_b = 4.2 \n",
        "features = nd.random.normal(scale=1, shape=(num_examples, num_inputs))\n",
        "labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + true_b \n",
        "labels += nd.random.normal(scale=0.01, shape=labels.shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bctqBaouaq_r",
        "colab_type": "code",
        "outputId": "2445c8a1-d49b-416c-d04a-de8336cba149",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "features[0], labels[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(\n",
              " [2.2122064 0.7740038]\n",
              " <NDArray 2 @cpu(0)>, \n",
              " [6.000587]\n",
              " <NDArray 1 @cpu(0)>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m9FrHOH0a5BP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def use_svg_display():\n",
        "    display.set_matplotlib_formats('svg')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eAbKPHx4bNsY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def set_figsize(figsize=(3.5, 2.5)):\n",
        "    use_svg_display()\n",
        "    plt.rcParams['figure.figsize'] = figsize"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gsJun0hqcC9X",
        "colab_type": "code",
        "collapsed": true,
        "outputId": "7f6bdc11-f1b8-4daf-97ae-51c60718178b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "help(plt.rcParams)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Help on RcParams in module matplotlib object:\n",
            "\n",
            "class RcParams(collections.abc.MutableMapping, builtins.dict)\n",
            " |  A dictionary object including validation\n",
            " |  \n",
            " |  validating functions are defined and associated with rc parameters in\n",
            " |  :mod:`matplotlib.rcsetup`\n",
            " |  \n",
            " |  Method resolution order:\n",
            " |      RcParams\n",
            " |      collections.abc.MutableMapping\n",
            " |      collections.abc.Mapping\n",
            " |      collections.abc.Collection\n",
            " |      collections.abc.Sized\n",
            " |      collections.abc.Iterable\n",
            " |      collections.abc.Container\n",
            " |      builtins.dict\n",
            " |      builtins.object\n",
            " |  \n",
            " |  Methods defined here:\n",
            " |  \n",
            " |  __getitem__(self, key)\n",
            " |      x.__getitem__(y) <==> x[y]\n",
            " |  \n",
            " |  __init__(self, *args, **kwargs)\n",
            " |      Initialize self.  See help(type(self)) for accurate signature.\n",
            " |  \n",
            " |  __iter__(self)\n",
            " |      Yield sorted list of keys.\n",
            " |  \n",
            " |  __len__(self)\n",
            " |      Return len(self).\n",
            " |  \n",
            " |  __repr__(self)\n",
            " |      Return repr(self).\n",
            " |  \n",
            " |  __setitem__(self, key, val)\n",
            " |      Set self[key] to value.\n",
            " |  \n",
            " |  __str__(self)\n",
            " |      Return str(self).\n",
            " |  \n",
            " |  copy(self)\n",
            " |      D.copy() -> a shallow copy of D\n",
            " |  \n",
            " |  find_all(self, pattern)\n",
            " |      Return the subset of this RcParams dictionary whose keys match,\n",
            " |      using :func:`re.search`, the given ``pattern``.\n",
            " |      \n",
            " |      .. note::\n",
            " |      \n",
            " |          Changes to the returned dictionary are *not* propagated to\n",
            " |          the parent RcParams dictionary.\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Data descriptors defined here:\n",
            " |  \n",
            " |  __dict__\n",
            " |      dictionary for instance variables (if defined)\n",
            " |  \n",
            " |  __weakref__\n",
            " |      list of weak references to the object (if defined)\n",
            " |  \n",
            " |  msg_backend_obsolete\n",
            " |  \n",
            " |  msg_depr\n",
            " |  \n",
            " |  msg_depr_ignore\n",
            " |  \n",
            " |  msg_depr_set\n",
            " |  \n",
            " |  msg_obsolete\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Data and other attributes defined here:\n",
            " |  \n",
            " |  __abstractmethods__ = frozenset({'__delitem__'})\n",
            " |  \n",
            " |  validate = {'_internal.classic_mode': <function validate_bool>, 'agg.p...\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Methods inherited from collections.abc.MutableMapping:\n",
            " |  \n",
            " |  __delitem__(self, key)\n",
            " |  \n",
            " |  clear(self)\n",
            " |      D.clear() -> None.  Remove all items from D.\n",
            " |  \n",
            " |  pop(self, key, default=<object object at 0x7f9d963a3060>)\n",
            " |      D.pop(k[,d]) -> v, remove specified key and return the corresponding value.\n",
            " |      If key is not found, d is returned if given, otherwise KeyError is raised.\n",
            " |  \n",
            " |  popitem(self)\n",
            " |      D.popitem() -> (k, v), remove and return some (key, value) pair\n",
            " |      as a 2-tuple; but raise KeyError if D is empty.\n",
            " |  \n",
            " |  setdefault(self, key, default=None)\n",
            " |      D.setdefault(k[,d]) -> D.get(k,d), also set D[k]=d if k not in D\n",
            " |  \n",
            " |  update(*args, **kwds)\n",
            " |      D.update([E, ]**F) -> None.  Update D from mapping/iterable E and F.\n",
            " |      If E present and has a .keys() method, does:     for k in E: D[k] = E[k]\n",
            " |      If E present and lacks .keys() method, does:     for (k, v) in E: D[k] = v\n",
            " |      In either case, this is followed by: for k, v in F.items(): D[k] = v\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Methods inherited from collections.abc.Mapping:\n",
            " |  \n",
            " |  __contains__(self, key)\n",
            " |  \n",
            " |  __eq__(self, other)\n",
            " |      Return self==value.\n",
            " |  \n",
            " |  get(self, key, default=None)\n",
            " |      D.get(k[,d]) -> D[k] if k in D, else d.  d defaults to None.\n",
            " |  \n",
            " |  items(self)\n",
            " |      D.items() -> a set-like object providing a view on D's items\n",
            " |  \n",
            " |  keys(self)\n",
            " |      D.keys() -> a set-like object providing a view on D's keys\n",
            " |  \n",
            " |  values(self)\n",
            " |      D.values() -> an object providing a view on D's values\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Data and other attributes inherited from collections.abc.Mapping:\n",
            " |  \n",
            " |  __hash__ = None\n",
            " |  \n",
            " |  __reversed__ = None\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Class methods inherited from collections.abc.Collection:\n",
            " |  \n",
            " |  __subclasshook__(C) from abc.ABCMeta\n",
            " |      Abstract classes can override this to customize issubclass().\n",
            " |      \n",
            " |      This is invoked early on by abc.ABCMeta.__subclasscheck__().\n",
            " |      It should return True, False or NotImplemented.  If it returns\n",
            " |      NotImplemented, the normal algorithm is used.  Otherwise, it\n",
            " |      overrides the normal algorithm (and the outcome is cached).\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Methods inherited from builtins.dict:\n",
            " |  \n",
            " |  __ge__(self, value, /)\n",
            " |      Return self>=value.\n",
            " |  \n",
            " |  __getattribute__(self, name, /)\n",
            " |      Return getattr(self, name).\n",
            " |  \n",
            " |  __gt__(self, value, /)\n",
            " |      Return self>value.\n",
            " |  \n",
            " |  __le__(self, value, /)\n",
            " |      Return self<=value.\n",
            " |  \n",
            " |  __lt__(self, value, /)\n",
            " |      Return self<value.\n",
            " |  \n",
            " |  __ne__(self, value, /)\n",
            " |      Return self!=value.\n",
            " |  \n",
            " |  __new__(*args, **kwargs) from builtins.type\n",
            " |      Create and return a new object.  See help(type) for accurate signature.\n",
            " |  \n",
            " |  __sizeof__(...)\n",
            " |      D.__sizeof__() -> size of D in memory, in bytes\n",
            " |  \n",
            " |  fromkeys(iterable, value=None, /) from abc.ABCMeta\n",
            " |      Returns a new dict with keys from iterable and values equal to value.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NLfd1kTucsha",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        }
      },
      "source": [
        "set_figsize()\n",
        "plt.scatter(features[:, 1].asnumpy(), labels.asnumpy(), 1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
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          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jO9su5_5cH95",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def data_iter(batch_size, features, labels):\n",
        "    num_examples = len(features)\n",
        "    indices = list(range(num_examples))\n",
        "    random.shuffle(indices)\n",
        "    for i in range(0, num_examples, batch_size):\n",
        "        j = nd.array(indices[i: min(i + batch_size, num_examples)])\n",
        "        yield features.take(j), labels.take(j)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PHhNGRVugN8X",
        "colab_type": "code",
        "outputId": "705f89ba-3253-4d80-a4dc-588b1cace922",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "source": [
        "batch_size = 10 \n",
        "for X, y in data_iter(batch_size, features, labels):\n",
        "    print(X, y)\n",
        "    break"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "[[-1.2064103  -0.5134857 ]\n",
            " [-0.09838036 -0.08484158]\n",
            " [ 0.69178855 -0.20333089]\n",
            " [ 0.3717077   0.9300072 ]\n",
            " [ 0.20530048 -1.9531958 ]\n",
            " [-0.42326602 -0.60970175]\n",
            " [-0.3075374   1.2168936 ]\n",
            " [ 0.7936089  -1.1430973 ]\n",
            " [-0.03442945 -0.11181278]\n",
            " [-0.08512726 -0.90995044]]\n",
            "<NDArray 10x2 @cpu(0)> \n",
            "[ 3.5305307  4.2787137  6.270785   1.7626053 11.2608595  5.427304\n",
            " -0.5507884  9.676742   4.515723   7.1249194]\n",
            "<NDArray 10 @cpu(0)>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3o2laxL1gY8I",
        "colab_type": "code",
        "outputId": "8f36dbac-c3bb-4a0a-bc82-fe2967fbaf7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        }
      },
      "source": [
        "for i in range(0, 100, 10):\n",
        "    print(i)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "10\n",
            "20\n",
            "30\n",
            "40\n",
            "50\n",
            "60\n",
            "70\n",
            "80\n",
            "90\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-PNohGjtD9RI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "w = nd.random.normal(scale=0.01, shape=(num_inputs, 1))\n",
        "b = nd.zeros(shape=(1, ))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOKj9UMlEQ1P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "w.attach_grad()\n",
        "b.attach_grad()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQUUgDZiEWjW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def linreg(X, w, b):\n",
        "    return nd.dot(X, w) + b "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wKCJrTQ3Efq9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def squared_loss(y_hat, y):\n",
        "    return (y_hat - y.reshape(y_hat.shape)) ** 2 / 2 "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sDGa_ew6E_EK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def sgd(params, lr, batch_size):\n",
        "    for param in params:\n",
        "        param[:] = param - lr * param.grad / batch_size "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LYKtEUigJ5F3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lr = 0.03 \n",
        "num_epochs = 3 \n",
        "net = linreg \n",
        "loss = squared_loss "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Itc333-oKyK2",
        "colab_type": "code",
        "outputId": "1575cdc0-10a6-4243-b615-61200ac32b31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "for epoch in range(num_epochs):\n",
        "    for X , y in data_iter(batch_size, features, labels):\n",
        "        with autograd.record():\n",
        "            l = loss(net(X, w, b), y)\n",
        "        l.backward()\n",
        "        sgd([w, b], lr, batch_size)\n",
        "    train_l = loss(net(features, w, b), labels)\n",
        "    print('epoch %d, loss %f' % (epoch+1, train_l.mean().asnumpy()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch 1, loss 0.040601\n",
            "epoch 2, loss 0.000153\n",
            "epoch 3, loss 0.000051\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ePEArrHNLsq6",
        "colab_type": "code",
        "outputId": "861f842d-452f-4b15-9f09-96c75b7c9da3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "true_w, w"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([2, -3.4], \n",
              " [[ 1.9993225]\n",
              "  [-3.3996203]]\n",
              " <NDArray 2x1 @cpu(0)>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ByYfsEBkM6jw",
        "colab_type": "code",
        "outputId": "849ddc7b-1b13-4220-8097-83d22016fec7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "true_b, b"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4.2, \n",
              " [4.1992807]\n",
              " <NDArray 1 @cpu(0)>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1CwqA3NLNraS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mxnet import autograd, nd \n",
        "\n",
        "num_inputs = 2 \n",
        "num_examples = 1000 \n",
        "true_w = [2, -3.4]\n",
        "true_b = 4.2 \n",
        "features = nd.random.normal(scale=1, shape=(num_examples, num_inputs))\n",
        "labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + true_b \n",
        "labels += nd.random.normal(scale=0.01, shape=labels.shape)\n",
        "\n",
        "# num_inputs = 2 \n",
        "# num_examples = 1000 \n",
        "# true_w = [2, -3.4]\n",
        "# true_b = 4.2 \n",
        "# features = nd.random.normal(scale=1, shape=(num_examples, num_inputs))\n",
        "# labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + true_b\n",
        "# labels += nd.random.normal(scale=0.01, shape=labels.shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqhKc5EOOvsz",
        "colab_type": "code",
        "outputId": "dfe836d6-111a-4ee0-8022-af866bdf1560",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "features[:10, 0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[-0.34304565  0.5086958  -0.491778   -0.15748753 -0.17884982  0.46760127\n",
              "  0.7615899  -0.5143139  -0.11494298  1.1116904 ]\n",
              "<NDArray 10 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "16-iNNWQO6Mm",
        "colab_type": "code",
        "outputId": "294aa42a-f52d-4dd6-8d6b-23d79a60fbb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "\n",
        "features[:10, 1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[-4.0720204e-01  9.1511744e-01 -1.5560924e+00 -5.2664566e-01\n",
              "  4.3164727e-01  1.7287086e-01  9.9106313e-05  1.7198472e+00\n",
              "  1.5617702e+00  1.4935805e+00]\n",
              "<NDArray 10 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h8ynSKipPEDi",
        "colab_type": "code",
        "outputId": "e449140a-c8c2-417a-ae04-12309db71640",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "labels[:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[ 4.902419   2.1094513  8.48928    5.673833   2.384568   4.539225\n",
              "  5.7347293 -2.681284  -1.342022   1.3345029]\n",
              "<NDArray 10 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JegnsTDaPwi_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mxnet.gluon import data as gdata\n",
        "\n",
        "batch_size = 10 \n",
        "dataset = gdata.ArrayDataset(features, labels)\n",
        "data_iter = gdata.DataLoader(dataset, batch_size, shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bksyH3gLSeUj",
        "colab_type": "code",
        "outputId": "7ab6181c-d25a-4339-d753-e32c5e0c395d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "source": [
        "for X, y in data_iter:\n",
        "    print(X, y)\n",
        "    break"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "[[ 5.8620876e-01 -1.2303159e+00]\n",
            " [ 2.0478384e+00 -5.9704506e-01]\n",
            " [ 1.6750511e+00 -5.1154160e-01]\n",
            " [-9.5759124e-01 -1.6360582e-01]\n",
            " [-1.1594498e+00 -1.6610742e+00]\n",
            " [-4.0538169e-02 -1.1355679e-01]\n",
            " [ 3.7723605e-02  7.1895683e-01]\n",
            " [-3.6949554e-04 -6.8584454e-01]\n",
            " [ 1.0389107e+00 -1.6013620e+00]\n",
            " [-5.8955443e-01 -5.7735205e-01]]\n",
            "<NDArray 10x2 @cpu(0)> \n",
            "[ 9.5580845 10.314247   9.272762   2.8271055  7.521756   4.506869\n",
            "  1.8416282  6.552679  11.740247   4.978527 ]\n",
            "<NDArray 10 @cpu(0)>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W5mJXTkESqdw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mxnet.gluon import nn \n",
        "\n",
        "net = nn.Sequential()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CFxuTOe2S8tn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net.add(nn.Dense(1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y_nvFCT2Ku5o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mxnet import init \n",
        "\n",
        "net.initialize(init.Normal(sigma=0.01))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aK4J2kHzLLWH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mxnet.gluon import loss as gloss \n",
        "\n",
        "loss = gloss.L2Loss()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EyY3TvlyLdYP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mxnet import gluon "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PapwD4ItLi_B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.03})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKUn3aEPL1Qe",
        "colab_type": "code",
        "outputId": "352b1fea-9a64-4916-8ef8-72fb3cdb1d8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "source": [
        "num_epochs = 20\n",
        "for epoch in range(1, num_epochs + 1):\n",
        "    for i, y in data_iter:\n",
        "        with autograd.record():\n",
        "            l = loss(net(X), y)\n",
        "        l.backward()\n",
        "        trainer.step(batch_size)\n",
        "    l = loss(net(features), labels)\n",
        "    print('epoch %d, loss: %f' % (epoch, l.mean().asnumpy()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch 1, loss: 5.061691\n",
            "epoch 2, loss: 5.533369\n",
            "epoch 3, loss: 6.879493\n",
            "epoch 4, loss: 6.334496\n",
            "epoch 5, loss: 8.212176\n",
            "epoch 6, loss: 7.439948\n",
            "epoch 7, loss: 7.466348\n",
            "epoch 8, loss: 8.417274\n",
            "epoch 9, loss: 8.601858\n",
            "epoch 10, loss: 7.198522\n",
            "epoch 11, loss: 6.927907\n",
            "epoch 12, loss: 7.851274\n",
            "epoch 13, loss: 8.246639\n",
            "epoch 14, loss: 8.035029\n",
            "epoch 15, loss: 8.349236\n",
            "epoch 16, loss: 7.813134\n",
            "epoch 17, loss: 6.822441\n",
            "epoch 18, loss: 7.859071\n",
            "epoch 19, loss: 8.180382\n",
            "epoch 20, loss: 8.247910\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GGt4a86YMm_d",
        "colab_type": "code",
        "outputId": "513b13f2-8b7f-481b-d526-b0aa821edc13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "dense = net[0]\n",
        "true_w, dense.weight.data())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([2, -3.4], \n",
              " [[0.03979441 0.11555748]]\n",
              " <NDArray 1x2 @cpu(0)>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oiyoGNsFOVTc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install d2lzh\n",
        "\n",
        "import d2lzh as d2l"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MI8kL8h-Zfvn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mxnet.gluon import data as gdata"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PtQZcvFPZumQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sys \n",
        "import time \n",
        "%matplotlib inline "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Te8yydx7qxGx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}